Search Results (1,255)

This study examines artificial intelligence (AI) not only as a student resource but as a pedagogical enabler—capable of operationalizing strategies such as context-based learning, narrative framing, and gamification that enhance motivation and relevance but are often difficult for instructors to sustain. By automating the generation of tailored scenarios, prompts, and examples, AI can make it feasible to embed these approaches consistently across large, multi-major classrooms. We applied this design in an undergraduate organic chemistry course for non-majors (N = 69) including Biomedical Laboratory Sciences, Nutrition, and Biology students. Organic chemistry for non-majors typically presents both conceptual challenges and low motivation due to limited career relevance, making this cohort well suited for examining AI-assisted pedagogies. Within this context, AI chatbot was integrated into chapter introductions, career-aligned scenarios, real-time activities, take-home assignments linking molecules to real-world contexts, and a game-based challenge—allowing the instructor to shift from sole source of personalization to a facilitator who guided and validated AI-generated materials. Surveys administered at the start and end of the semester revealed notable gains: student interest in organic chemistry increased from 42.0% to 73.3%, perceived relevance to majors rose from 24.6% to 85.0%, and importance for careers grew from 20.3% to 83.3%. Feedback after each activity indicated stronger awareness of real-life applications, greater confidence, and appreciation for AI’s role in making chemistry approachable. Students valued the clarity of introductions, the applied focus of the “Celebrity Molecules” assignment, and the engaging, collaborative nature of the game. Findings suggest AI can make evidence-based strategies more feasible and scalable, enhancing motivation and relevance in courses where students often struggle. Future work should examine long-term learning outcomes and transferability across disciplines. Full article

Mortise and tenon joints are a core technique in ancient Chinese architecture and an important form of extant intangible cultural heritage (ICH). However, despite growing digital adoption for ICH preservation, limited empirical evidence exists on how virtual reality (VR) serious games affect user attitudes and ICH transmission, particularly in complex manual construction such as mortise and tenon joints. This study develops and evaluates a VR gamified learning system based on the six-column Luban lock to examine its role in preserving and transmitting applied ICH. Two studies were conducted: Study 1 focused on the design of the VR system, and Study 2 involved an empirical evaluation, recruiting 14 college students for structured interviews and 305 participants for a questionnaire, analyzed using reliability and validity tests and a four-quadrant model. The analysis revealed that the questionnaire showed excellent internal consistency (Cronbach’s alpha = 0.98) and good construct validity (KMO = 0.97). Most indicators for subject selection, game design, and VR format were in the “advantage” and “maintenance” zones of the four-quadrant model. This supports the hypothesis that these design factors are positively associated with user attitudes and the perceived effectiveness of ICH protection. These results suggest that VR-gamified learning offers a scalable template for digitally protecting and disseminating ICH skills. Full article

Design Thinking (DT) has been widely promoted as a creative, human-centred approach for engaging students with real-world problems. Yet, research consistently shows that DT in mainstream schooling often struggles with ambiguity, superficial engagement with socio-scientific issues, weak integration of disciplinary knowledge, and epistemological tensions with school learning. In this paper, we examine whether DT can become more effective and educationally meaningful when enacted through a socio-constructionist environment using digital media as both design tools and design products. Drawing on a school-based intervention with 70 students using ChoiCo—an open-source digital authoring system for creating socio-scientific games—we analysed critical incidents of student interaction to explore how constructionist digital media mediate reasoning, collaboration, and conceptual development. Our findings show that ChoiCo supports conceptual clarity, iterative refinement, and epistemic grounding by requiring students to encode ideas into rules, thresholds, and consequences. The system’s malleability and embedded feedback align with a special socio-constructionist DT model developed through a multi-organisational European Research and Innovation Project ExtenDT2, enabling rapid prototyping and collaborative meaning-making. We argue that socio-constructionist DT offers a promising way to address long-standing shortcomings of DT in education, shifting the focus from producing polished artefacts to engaging in meaningful, iterative, and epistemically rich design activity. Implications for curriculum design, teacher practice, and the integration of constructionist digital media in DT pedagogy are discussed. Full article

With limited energy constraints, the issue of transmission and interference strategies have received considerable critical attention in cyber–physical security. In this paper, for remote state estimation under signal-to-interference-plus-noise ratio-based denial-of-service (DoS) attacks, the Stackelberg game between the sensor and the attacker is investigated. To balance estimation performance and energy consumption, the two players determine the transmission power and interference power sequentially under an incomplete information structure where the sensor does not know the fading channel gain of the attacker exactly. The schedule problem over the infinite-time horizon is first formulated as a Markov decision process with finite state and action spaces. Then, a Bayesian Stackelberg game (BSG) is constructed by incorporating the probability information of the channel interference gain. Based on the definition of best-response, the solution of the BSG is presented and the existence of the Stackelberg equilibrium is proven. Furthermore, a Stackelberg Q-learning algorithm is used to obtain the optimal strategies for the two players. Numerical results demonstrate the effectiveness of the proposed game method when the sensor is unable to access an attacker’s channel gain information. Full article

In the digital era, industries increasingly demand innovation and problem-solving capabilities, making cross-disciplinary integration and creative thinking essential competencies for information management professionals. Although previous studies have shown that Problem-Based Learning (PBL) enhances students’ problem-solving abilities and proactive learning behaviors, its effectiveness in supporting creative extension and conceptual deepening remains limited without structured thinking frameworks. To address this issue, this study integrated PBL with a Two-Tier Mandala Thinking approach based on a nine-grid structure. The proposed method combines first-tier divergent thinking with second-tier spiral convergence to guide students in establishing conceptual foundations, differentiating ideas, and refining design directions. A quasi-experimental study was conducted in a course in which students completed a game design task using either the Two-Tier Mandala Thinking Method or conventional brainstorming strategies. Quantitative results indicate that students in the Mandala Thinking group significantly outperformed those in the brainstorming group across three learning performance metrics. Qualitative findings further revealed that students using the proposed approach exhibited enhanced creative self-efficacy and greater confidence in their creative outcomes. Overall, integrating Two-Tier Mandala Thinking into PBL effectively supported the experimental group in structuring and developing in-depth creative thinking processes, providing empirical evidence for its application in innovation-oriented information education. Full article

This work explores user needs for educational games and gamification that incorporates Generative Artificial Intelligence (GenAI). As GenAI is increasingly incorporated in educational settings, we must consider both the wide-spanning literature on gamification and games that have been shown to benefit learning, and characterize the needs and desires of relevant stakeholders in developing educational games that incorporate GenAI generally, and specifically for higher education. A mixed-methods questionnaire inquired 345 undergraduate students about their perceptions, use patterns, needs, and desires related to GenAI, educational and non-educational games, and text-based games. GenAI tools are widely used for educational purposes already, but mostly as a supplementary source. Despite the wide use, participants expressed being concerned with accuracy, transparency, and quality. Participants also expressed a desire for an educational game/tool to have scaffolded interactions and to help with learning material in math, science, and language arts. Taken together the findings provide a road map and specific recommendations for developing an educational game incorporating GenAI. The roadmap includes instructional design (i.e., the gamified tools’ content and type(s) of instruction and interaction) through information regarding preferred platforms, game genres, gamified properties (e.g., characters, challenges), and lastly, clear information about concerns students have related to trust and equity that will need to be addressed in an educational game incorporating GenAI. Full article

Background: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, motor hyperactivity and verbal and cognitive impulsivity. Impairments in executive functions (EFs), in particular working memory, monitoring and organization of daily life, are frequently observed in children diagnosed with ADHD, and are reflected in behavioural, social-emotional and learning difficulties. The development and use of technologies such as virtual reality (VR), augmented reality (AR) and mixed reality (MR) for ADHD have increased in recent years, using a variety of tools to support including PC, video games, wearable devices and tangible interfaces. Objectives: To systematically map the current state of research on the use of AR, VR and MR technologies to assess and/or enhance EFs in children with ADHD. To evaluate the effects on their quality of life and on families’ and caregivers’ burden reduction. To explore the interventions’ clinical validity. Methods: A scoping review according to PRISMA-ScR guidelines was conducted. A systematic search was carried out in the Scopus and Web of Science databases for studies published between 2015 and 2025. Empirical studies published in English that examined children with ADHD aged <13 years were included. AR-, VR-, or MR-based interventions focused on EF were considered. For each study, the following features were recorded: year and country of publication, design, objectives, EFs considered, technology and hardware used, main results, and limitations. Results: Twenty studies were identified. The most frequently addressed functional domains were sustained and selective visual attention, working memory, and inhibition. Assessment interventions primarily involved the use of a head-mounted display (HMD) in conjunction with the Continuous Performance Test (CPT). Training interventions included immersive VR, serious video games, VR with motor or dual-task training, and MR. The results suggest that VR can enhance cognitive performance and sustained attention; however, longitudinal studies are required to evaluate its long-term effectiveness and integrate emotional skills. Conclusions: The use of these technologies is a promising strategy for the assessment and training of EFs in children with ADHD. These tools provide positive, inclusive feedback and motivating tasks. Nevertheless, larger sample studies and longitudinal follow-ups to confirm the suitability and effectiveness of the technology-based programs are warranted. Full article

Reinforcement learning (RL) is widely used to learn sequential decision-making policies in complex environments. Deep Q-network (DQN) extends Q-Learning using deep neural networks, enabling learning in high-dimensional state spaces. However, conventional DQN-based approaches can exhibit variability in learning stability and convergence speed even under similar training conditions. Random Ensemble Mixture (REM) has been introduced to improve stability by combining multiple Q-value estimates, but it typically requires running multiple models simultaneously, which increases computational cost. This paper proposes an enhanced DQN method that integrates REM with a Weight Referring and Merging (WRM) mechanism to improve training stability and efficiency. The proposed approach updates a single primary agent using standard DQN learning while maintaining diversity among auxiliary agents by selectively referring to and partially merging weights from the primary network. Q-values from the primary and auxiliary agents are then combined through REM to produce the final value estimate for action selection. Experiments in the Catch Game environment indicate that the proposed method reaches stable performance earlier than a baseline DQN and reduces training time under the tested configuration (approximately 78%). While the results are encouraging in this environment, further evaluation on additional benchmarks is required to assess broader applicability. Full article

In higher education, maintaining student engagement in voluntary activities remains a challenge. While serious games are recognized for enhancing motivation, evidence regarding their long-term effectiveness in non-graded contexts is limited. This study evaluates the impact of “The Time Machine”, an interactive branching-narrative serious game, on academic motivation and participation. A quasi-experimental study was conducted across 14 undergraduate courses. The Motivated Strategies for Learning Questionnaire-Short Form (MSLQ-SF) was administered in three surveys (Survey 1, Survey 2, Survey 3). In total, 404 students completed at least one survey (635 questionnaire records). Longitudinal analyses (Friedman test) were conducted on the complete-case sample (n = 65) comprising students who responded to all three surveys and revealed no statistically significant changes in motivational dimensions. Completion rates (defined as responding to all three surveys) were significantly dependent on the implementation context (Fisher’s test, p < 0.001), being higher in groups with direct instructional support. Additionally, female students reported significantly higher test anxiety than males, while prior affinity for video games showed no influence on motivational outcomes. Narrative-driven serious games can sustain motivation over time effectively. However, their success relies critically on pedagogical scaffolding and teacher involvement rather than solely on game mechanics or students’ gamer profiles. Full article

Medical education requires learners to integrate complex basic science knowledge with clinical reasoning, with endocrinology posing particular challenges due to nonlinear feedback and system-level interactions. Although serious games may enhance learning, many implementations are individual/solitary, and evidence for analog serious trading card games is limited. This study evaluated a mnemonic-driven Medimon Learning Card Game (LCG) designed to support systems-based endocrine education. A quasi-experimental study was conducted with first-year medical students during an endocrine course block. All students received identical instruction, while a subset participated in a guided, competitive Medimon LCG session. Achievement was assessed using a pretest and a delayed posttest administered two weeks after the intervention, along with course examination performance. Engagement was measured using the Situational Interest Survey for Multimedia (SIS-M) and open-ended responses. Students who participated in the Medimon LCG demonstrated significantly greater delayed learning gains than controls, while course examination performance did not differ between groups. SIS-M results indicated high levels of interest and perceived value, and qualitative findings highlighted affective engagement, cognitive reinforcement, and social interaction. These findings suggest that an analog serious trading card game can enhance engagement and support longer-term retention of complex endocrine concepts, offering a transferable framework for socially mediated game-based learning in medical education. Full article

This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, and utility assets, over the period from November 2018 to March 2023. Following data preprocessing, synchronization across data sources, and the construction of history-dependent features, the analysis focuses on a final analytical sample of approximately 70,000 transactions. To address the challenges of non-fungibility, thin trading, and high price dispersion, we develop an interpretable predictive framework that integrates domain-informed manual feature engineering, automated Deep Feature Synthesis, and dimensionality reduction via Principal Component Analysis. Three non-linear models—Random Forest, XGBoost, and a Multilayer Perceptron—are trained and evaluated using both random and time-aware validation strategies. The results indicate that XGBoost consistently achieves the highest predictive accuracy, both overall and across individual NFT segments, while historical transaction prices emerge as the dominant predictor of future prices. Segment-level analysis reveals substantial heterogeneity in predictability, with art and collectible NFTs exhibiting more stable pricing patterns than gaming and metaverse assets. Overall, the findings highlight strong path dependence and reputation-driven valuation in NFT markets and demonstrate that carefully designed machine-learning models can deliver high predictive performance without sacrificing economic interpretability. Full article

Distributed production systems have to increasingly balance economic goals such as energy efficiency and productivity with critical technical requirements such as flexibility, real-time capability, and reliability. This paper presents a novel approach for distributed optimization by means of Evolutionary State-based Potential Games with dynamic grid structures. More in detail, we leverage the combination of Potential Games which provide rigorous convergence guarantees with population-based optimization to improve the efficiency of the learning process. Specifically, we address challenges of previous approaches including inefficient best response strategies, insufficient coverage of the state–action space and the lack of knowledge transfer among agents. The developed strategies are evaluated on a industrial system of laboratory scale. The results highlight advances in evolutionary state-based knowledge transfer and an improved coverage resulting in efficient control policies. By leveraging dynamic grid structures, Evolutionary State-based Potential Games enable the maximization of weighted production targets while simultaneously eliminating process losses resulting in improvements in the considered metrics compared to state-of-the-art methods. Full article

Connect-4, a solved two-player perfect-information game, offers a compact benchmark for artificial intelligence research due to its strategic depth and structural regularities, including board symmetries. This review presents a taxonomy-driven synthesis of Connect-4 AI research, encompassing game-theoretical foundations, classical search algorithms, reinforcement learning methods, explainable AI, and formal verification approaches. Analysis of search-, learning-, and hybrid-based methods reveals three dominant patterns: (i) classical search techniques prioritize determinism and efficiency but face scalability limits; (ii) reinforcement learning and neural approaches improve adaptability at the cost of interpretability and computational resources; and (iii) explainable and formally verified frameworks enhance transparency and reliability while imposing additional performance constraints. Recent advances in Connect-4 AI are driven less by raw performance gains than by strategic integration of efficiency, adaptability, interpretability, and robustness. Structuring the literature through a multidimensional taxonomy clarifies conceptual relationships, highlights underexplored research intersections, and points to emerging trends, including hybrid search–learning systems and explainable game intelligence. Overall, Connect-4 serves as a concise experimental domain for investigating fundamental challenges in game-playing AI, system design, and human–AI interaction. Full article

Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain–Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant’s subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting. Full article

Financial education is critical for fostering inclusive, resilient societies by reducing inequalities and promoting sustainable development. Despite growing interest, there is a notable gap in research on effective pedagogical approaches for financial literacy in primary education. Early intervention is essential to shape financially literate, active citizens capable of contributing to sustainable communities. This study addresses this gap by comparing the effectiveness of two distinct implementation packages involving active pedagogies—cooperative learning (CL) with high autonomy and stable groups, and game-based learning (GBL) featuring stronger teacher orchestration and dynamic grouping—in enhancing financial literacy among primary school pupils. Using a multiple case study design, two fifth-grade classes in Northern Italy participated in six 2 h financial education sessions, each employing a different instructional implementation package. Quantitative data from pre- and post-tests revealed significant improvements in financial literacy in both groups, confirmed by Wilcoxon signed-rank tests, with CL-based implementation showing a larger observed effect size (Hedges’g = 1.84) than GBL-based implementation (g = 1.20). Qualitative analysis of focus groups showed that CL-based implementation, characterized by high autonomy and group stability, fostered deeper learning through collaboration, shared responsibility, and relational skills vital for social sustainability. In contrast, GBL-based implementation, with structured teacher facilitation, promoted context-specific knowledge and relied more on extrinsic motivation and competition. These findings suggest that the observed benefits may be associated with the combined features of the cooperative learning-based implementation package, particularly high autonomy and stable group structures, which appear to support more socially embedded financial literacy. They also highlight both the potential and the limitations of game-based approaches. The study offers evidence-based insights for designing effective financial education programs that support both cognitive and social competencies in primary education. Full article